# FIESTA - Photo-Based Module

## Photo-based (PB) module overview

FIESTA’s Photo-Based (PB) module calculates population estimates and associated sampling errors based on Patterson (2012). In contrast to FIA’s traditional green-book estimators which were constructed based on the finite sampling paradigm using sample plots with distinct area, the photo-based estimators were constructed based on the context of the infinite sampling paradigm, along with the concept of a support region. The sample is the set of plot centers and the information from the support region (the photo plot) are assigned to the plot centers. The photo interpreted points are used as a sample of the support region and the observations are used to estimate the information from the support region. FIESTA includes non-ratio estimators for area and percent cover estimates by domain, and ratio-of-means estimators for area and percent cover estimates within domain, and supports post-stratification for reducing variance.

## Objective of tutorial

The main objective of this tutorial is to demonstrate how to use FIESTA for generating photo-based estimates, supplementary to FIA’s traditional estimates, using estimators from Patterson (2012). For information on FIESTA parameters or population data, please see the FIESTA_manual_mod_est and FIESTA_manual_mod_pop vignettes. The structure of this vignette is as follows:

## Output values from FIESTA Photo-Based module

Estimates with percent sampling error for the row domain (and column domain) specified by the input parameters. This can be in the form of one table or two separate tables, depending on the number of domains and on allin1 parameter specified through the table_opts parameter.

FIESTA returns a list object with one or more of the following components. If savedata = TRUE, all output data frames are written to the specified outfolder.

• $est - Data frame with estimates by rowvar, colvar (and estimation unit). If sumunits = TRUE or one estimation unit and colvar = NULL, estimates and percent sampling error are all in est. •$pse - Data frame with percent sampling errors corresponding to est.
• $titlelst - If returntitle = TRUE, a list with one or two titles for est and pse, depending on number of output data frames. •$raw - If rawdata = TRUE, a list of raw data used in the estimation process.

### Raw Data Used for Producing Estimates (If rawdata = TRUE)

View Raw Data Used for Producing Estimates
• raw$pntsampcnt - Number of points by domain for estimate. • raw$stratdat - Data frame of strata information by estimation unit. See table below for variable descriptions. Acres is only included if tabtype = "AREA").
• raw$pltdom - Proportion of points by domain by plot, strata, estimation unit. Description of variables in stratdat. Variable Description ESTN_UNIT Estimation unit STRATUMCD Strata value P1POINTCNT Number of pixels by strata and estimation unit n.strata Number of plots in strata (and estimation unit) n.total Number of plots for estimation unit ACRES Total acres for estimation unit strwt Summed proportions by strata and estimation unit Description of variables in pltdom. Variable Description ESTN_UNIT Estimation unit STRATUMCD Strata value plot_id Unique identifier for ICE plot category Category (domain) for estimation nbrpts.pltdom Number of points by category (domain) PtsPerPlot Number of points interpreted p.pltdom Proportion of plot by category ### Processing data (If rawdata = TRUE) View Processing Data Separate data frames with calculated variables used in estimation process. The number of processing tables depends on the input parameters. The tables include: • raw$unit_totest - Total by estimation unit
• raw$unit_rowest - If rowvar != NULL, rowvar totals • raw$unit_colvar - If colvar != NULL, colvar totals
• raw$unit_grpvar - If colvar != NULL, a combination of rowvar and colvar And, if sumunits = TRUE, the raw data for the summed estimation units are also included: (totest, rowest, colest, grpest, respectively). These tables do not included estimate proportions (nhat and nhat.var). See below for variable descriptions of summed estimation units: Description of variables in processing tables for nonratio estimates. Variable Description phat Estimated proportion of land phat.var Variance estimate of estimated proportion of land phat.se Standard error of estimated proportion of land { sqrt(phat.var) } phat.cv Coefficient of variance of estimated proportion of land { phat.se/phat } est Estimated percent cover of land { phat*100 } est.var Variance of estimated percent cover of land { phat.var*100^2 } Description of variables in processing tables for ratio estimates. Variable Description phat.n Estimated proportion of land, for numerator phat.var.n Variance of estimated proportion of land, for numerator phat.d Estimated proportion of land, for denominator phat.var.d Variance of estimated proportion of land, for denominator covar Covariance of estimated proportion of numerator and denominator rhat Ratio of estimated proportions (numerator/denominator) rhat.var Variance of ratio of estimated proportions rhat.se Standard error of ratio of estimated proportions { rhat.se/rhat } rhat.cv Coefficient of variation of ratio of estimated proportions { sqrt(rhat.var) } est Estimated percent cover of land { rhat*100 } est.var Variance of estimated percent cover of land { rhat.var*100^2 } Description of variables in processing tables for both nonratio and ratio estimates. Variable Description nbrpts Number of points used in estimate ACRES Total acres for estimation unit (if tabtype=‘AREA’) est.se Standard error of estimated percent cover of land { sqrt(est.var) } est.cv Coefficient of variance of estimated percent cover of land { est.se/est } pse Percent sampling error of the estimated percent cover of land { est.cv*100 } Description of variables in processing tables for all estimates. Variable Description CI99left Left tail of 99% confidence interval for estimate { est - (2.58*est.se) } CI99right Right tail of 99% confidence interval for estimate { est + (2.58*est.se) } CI95left Left tail of 95% confidence interval for estimate { est - (1.96*est.se) } CI95right Right tail of 95% confidence interval for estimate { est + (1.96*est.se) } CI68left Left tail of 68% confidence interval for estimate { est - (0.97*est.se) } CI68right Right tail of 68% confidence interval for estimate { est + (0.97*est.se) } ## PB Functions and Examples The examples following use data from the Image-Based Change Estimation (ICE) project, from two counties, or Estimation Units (ESTN_UNIT) in the state of Utah: Davis (11); Salt Lake (35). The ICE project is an image-based inventory across FIA plots designed to supplement the FIA field-based inventory for monitoring land use and land cover change at a more timely interval than the current FIA reporting timeframe. Observations are made at two points in time across all FIA plots and point-level interpretations are made within an acre support region from plot center. Attributes of land use, land cover, change, and agent of change are recorded at each point. The dataset includes plot-level and point-level data for each plot in the sample. The following tutorial uses a subset of ICE data to demonstrate how to generate estimates from the modPB() function. #### Example data - Utah, Davis and Salt Lake counties (ut1135), Time interval 2011-2014 External data Description icepnt_utco1135.csv ICE point-level data (see ref_icepnt R data frame for variable descriptions) icepctcover_utco1135.csv ICE plot-level percentages of land cover icepltassgn_utco1135.csv ICE plot-level data, including estimation unit and strata variables cover_LUT.csv ICE look-up table for land cover classes chg_ag_LUT.csv ICE look-up table for change agent classes unitarea_utco1135.csv Area, in acres, by county estimation unit (ESTN_UNIT) strlut_utco1135.csv Pixel counts by strata (STRATUMCD) and estimation unit (ESTN_UNIT) ### Set up First, you’ll need to load the FIESTA library: library(FIESTA) Next, you’ll need to set up an “outfolder”. This is just a file path to a folder where you’d like FIESTA to send your data output. For our purposes in this vignette, we have saved our outfolder file path as the outfolder object in a temporary directory. We also set a few default options preferred for this vignette. outfolder <- tempdir() ### Get data for examples View Getting Data Now that we’ve loaded FIESTA and setup our outfolder, we can retrieve the data needed to run the examples. First, we point to some external data stored in FIESTA and import into R. ## Get external data file names icepntfn <- system.file("extdata", "PB_data/icepnt_utco1135.csv", package = "FIESTA") icepltfn <- system.file("extdata", "PB_data/icepltassgn_utco1135.csv", package = "FIESTA") icepctcoverfn <- system.file("extdata", "PB_data/icepctcover_utco1135.csv", package = "FIESTA") icechg_agfn <- system.file("extdata", "PB_data/chg_ag_LUT.csv", package = "FIESTA") icecoverfn <- system.file("extdata", "PB_data/cover_LUT.csv", package = "FIESTA") unitareafn <- system.file("extdata", "PB_data/unitarea_utco1135.csv", package = "FIESTA") strlutfn <- system.file("extdata", "PB_data/strlut_utco1135.csv", package = "FIESTA") icepnt <- read.csv(icepntfn) iceplt <- read.csv(icepltfn) icepctcover <- read.csv(icepctcoverfn) icecover <- read.csv(icecoverfn) icechg_ag <- read.csv(icechg_agfn) str(icepnt, max.level = 1) output ## 'data.frame': 1305 obs. of 8 variables: ##$ plot_id   : num  5.54e+12 5.54e+12 5.54e+12 5.54e+12 5.54e+12 ...
##  $dot_cnt : int 1 26 31 36 41 1 26 31 36 41 ... ##$ change_1_2: int  0 0 0 0 0 0 0 0 0 0 ...
##  $cover_1 : int 310 310 310 310 310 140 220 140 220 140 ... ##$ cover_2   : int  310 310 310 310 310 140 220 140 220 140 ...
##  $use_1 : int 200 200 200 200 200 400 400 400 400 400 ... ##$ use_2     : int  200 200 200 200 200 400 400 400 400 400 ...
##  $chg_ag_2 : int 0 0 0 0 0 0 0 0 0 0 ... str(iceplt, max.level = 1) output ## 'data.frame': 133 obs. of 5 variables: ##$ plot_id   : num  5.54e+12 5.54e+12 5.54e+12 5.54e+12 5.54e+12 ...
##  $ESTN_UNIT : int 11 11 11 11 11 11 11 35 35 35 ... ##$ STRATUMCD : int  2 2 2 2 2 2 2 2 1 2 ...
##  $LON_PUBLIC: num -112 -112 -112 -112 -112 ... ##$ LAT_PUBLIC: num  41 41.1 41 41 41 ...
str(icepctcover, max.level = 1)
output
## 'data.frame':    133 obs. of  15 variables:
##  $plot_id : num 5.54e+12 5.54e+12 5.54e+12 5.54e+12 5.54e+12 ... ##$ Change           : int  0 0 0 0 0 0 0 0 0 1 ...
##  $Tree11 : int 0 0 0 0 0 0 40 20 0 0 ... ##$ Shrub11          : int  0 0 20 0 0 0 0 80 0 0 ...
##  $OtherVegetation11: int 0 60 60 0 0 0 60 0 100 27 ... ##$ Barren11         : int  0 0 20 0 0 0 0 0 0 56 ...
##  $Impervious11 : int 0 40 0 0 0 0 0 0 0 18 ... ##$ Water11          : int  100 0 0 100 100 100 0 0 0 0 ...
##  $Tree14 : int 0 0 0 0 0 0 40 20 0 0 ... ##$ Shrub14          : int  0 0 20 0 0 0 0 80 0 0 ...
##  $OtherVegetation14: int 0 60 60 0 0 0 60 0 100 24 ... ##$ Barren14         : int  0 0 20 100 100 0 0 0 0 36 ...
##  $Impervious14 : int 0 40 0 0 0 0 0 0 0 40 ... ##$ Water14          : int  100 0 0 0 0 100 0 0 0 0 ...
##  $Veg.NonVeg : int 0 0 0 0 0 0 0 0 0 3 ... Next, we can convert X/Y coordinates to a simple feature and look at the spatial distribution by county (ESTN_UNIT) with the spMakeSpatialPoints() function from FIESTA. icepltsp <- spMakeSpatialPoints(xyplt = iceplt, xy.uniqueid = "plot_id", xvar = "LON_PUBLIC", yvar = "LAT_PUBLIC", xy.crs = 4269) plot(icepltsp["ESTN_UNIT"]) plot Now, let’s look at the look up tables stored in FIESTA for land use cover codes and change agent codes and create new lookup tables for Time 1 and Time 2 land use cover. icecover output ## cover cover_nm ## 1 110 Tree ## 2 130 Shrub ## 3 140 OtherVegetation ## 4 210 Barren ## 5 220 Impervious ## 6 310 Water ## 7 999 Uninterpretable icechg_ag output ## chg_ag_2 chg_ag_2_nm ## 1 0 No Change ## 2 11 Development ## 3 21 Harvest (Forested: >10% canopy cover visible on T2) ## 4 22 Harvest (Forested: <10% canopy cover visible on T2) ## 5 31 Regeneration of Vegetation ## 6 33 Removal or Loss of Vegetation ## 7 34 Stress or Mortality ## 8 41 Fire ## 9 90 Expected Change ## 10 91 Other Change ## 11 99 Uninterpretable # Create look-up tables for Time 1 (cover_11) and Time 2 (cover_14) classes icecover_1 <- icecover names(icecover_1) <- sub("cover", "cover_1", names(icecover_1)) icecover_2 <- icecover names(icecover_2) <- sub("cover", "cover_2", names(icecover_2)) icecover_1 output ## cover_1 cover_1_nm ## 1 110 Tree ## 2 130 Shrub ## 3 140 OtherVegetation ## 4 210 Barren ## 5 220 Impervious ## 6 310 Water ## 7 999 Uninterpretable icecover_2 output ## cover_2 cover_2_nm ## 1 110 Tree ## 2 130 Shrub ## 3 140 OtherVegetation ## 4 210 Barren ## 5 220 Impervious ## 6 310 Water ## 7 999 Uninterpretable Next, let’s import and look at the stratification information stored in FIESTA. ## Area by estimation unit unitarea <- read.csv(unitareafn) unitarea output ## ESTN_UNIT ACRES ## 1 11 405566 ## 2 35 516977 ## Pixel counts by strata classes strlut <- read.csv(strlutfn) strlut output ## ESTN_UNIT STRATUMCD P1POINTCNT ## 1 11 2 26266 ## 2 35 1 9050 ## 3 35 2 24432 ### PB Module The following examples are set up into two sections as follows where modPBpop() contains an example which sets up the data for estimation in modPB(). ### modPBpop() FIESTA’s population functions (mod*pop) check input data and perform population-level calculations, such as: summing number of sampled plots and standardizing auxiliary data. These functions are specific to each FIESTA module and are run prior to or within a module for any population of interest. #### Population Example 1: Population Data for estimation of percent of land cover at Time 1 (2011) These population data are used in: Example 1. View Example For FIESTA’s PB Module, the modPBpop() function calculates and outputs: number of plots by strata. The output from modPBpop() can be used for one or more estimates from modPB(). Here, we set up our population data for the following examples. We simply supply a few key arguments and we have our population data: # Percent land cover at Time 1 (2011) for all land in Davis and Salt Lake Counties, UT PBpopdat <- modPBpop(pnt = icepnt, pltassgn = iceplt, pltassgnid = "plot_id", pntid = "dot_cnt") names(PBpopdat) output ## [1] "PBx" "pltassgnx" "plotid" "pntid" "pltassgnid" ## [6] "sumunits" "unitvar" "unitvars" "strata" "strtype" ## [11] "stratalut" "strvar" "strwtvar" "plotsampcnt" "getprop" Note that the modPBpop() function returns a list with lots of information and data for us to use. For a quick look at what this list includes we can use the str() function: str(PBpopdat, max.level = 1) output ## List of 15 ##$ PBx        :Classes 'data.table' and 'data.frame':    1305 obs. of  12 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr [1:2] "plot_id" "dot_cnt"
##  $pltassgnx :Classes 'data.table' and 'data.frame': 133 obs. of 3 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr "plot_id" ##$ plotid     : chr "plot_id"
##  $pntid : chr "dot_cnt" ##$ pltassgnid : chr "plot_id"
##  $sumunits : logi FALSE ##$ unitvar    : chr "ONEUNIT"
##  $unitvars : chr "ONEUNIT" ##$ strata     : logi FALSE
##  $strtype : chr "POST" ##$ stratalut  :Classes 'data.table' and 'data.frame':    1 obs. of  5 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr [1:2] "ONEUNIT" "ONESTRAT"
##  $strvar : chr "ONESTRAT" ##$ strwtvar   : chr "strwt"
##  $plotsampcnt: NULL ##$ getprop    : logi TRUE

Now that we’ve created our population dataset, we can move on to estimation.

#### Population Example 2: Population data for estimation of area of land cover at Time 1 (2011)

These population data are used in: Example 2, Example 3, Example 4, Example 5, Example 7, and Example 8.

View Example

Here we create population data in order to estimate area, in acres, of land cover at Time 1 (2011) for all land in Davis and Salt Lake Counties, Utah.

First, since we want to get estimates for the total population, let’s sum the area for both counties.

# read in file

# sum up the acres
sum(unitarea$ACRES) output ## [1] 922543 Next, we add the total area to the modPBpop function PBpoparea <- modPBpop(pnt = icepnt, pltassgn = iceplt, pltassgnid = "plot_id", pntid = "dot_cnt", unitarea = sum(unitarea$ACRES)) # using the total number of acres

We can look at the contents of the output list. The output now includes unitarea, the total acres for the population of two counties.

str(PBpoparea, max.level = 1)
output
## List of 18
##  $PBx :Classes 'data.table' and 'data.frame': 1305 obs. of 12 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr [1:2] "plot_id" "dot_cnt" ##$ pltassgnx  :Classes 'data.table' and 'data.frame':    133 obs. of  3 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "plot_id"
##  $plotid : chr "plot_id" ##$ pntid      : chr "dot_cnt"
##  $pltassgnid : chr "plot_id" ##$ sumunits   : logi FALSE
##  $unitvar : chr "ONEUNIT" ##$ unitvars   : chr "ONEUNIT"
##  $strata : logi FALSE ##$ strtype    : chr "POST"
##  $stratalut :Classes 'data.table' and 'data.frame': 1 obs. of 5 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr [1:2] "ONEUNIT" "ONESTRAT" ##$ strvar     : chr "ONESTRAT"
##  $strwtvar : chr "strwt" ##$ plotsampcnt: NULL
##  $getprop : logi TRUE ##$ unitarea   :Classes 'data.table' and 'data.frame':    1 obs. of  2 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "ONEUNIT"
##  $areavar : chr "AREA" ##$ areaunits  : chr "acres"

#### Population Example 3: Population data for estimation by estimation unit

These population data are used in: Example 6.

View Example

Here, we generate population data in order to produce estimates by each estimation unit (i.e, County).

PBpopunit <- modPBpop(pnt = icepnt,
pltassgn = iceplt,
pltassgnid = "plot_id",
pntid = "dot_cnt",
unitarea = unitarea,
unitvar = "ESTN_UNIT")
names(PBpopunit)
output
##  [1] "PBx"         "pltassgnx"   "plotid"      "pntid"       "pltassgnid"
##  [6] "sumunits"    "unitvar"     "unitvars"    "strata"      "strtype"
## [11] "stratalut"   "strvar"      "strwtvar"    "plotsampcnt" "getprop"
## [16] "unitarea"    "areavar"     "areaunits"

Again, we can look at the contents of the output list.

str(PBpopunit, max.level = 1)
output
## List of 18
##  $PBx :Classes 'data.table' and 'data.frame': 1305 obs. of 11 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr [1:2] "plot_id" "dot_cnt" ##$ pltassgnx  :Classes 'data.table' and 'data.frame':    133 obs. of  3 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "plot_id"
##  $plotid : chr "plot_id" ##$ pntid      : chr "dot_cnt"
##  $pltassgnid : chr "plot_id" ##$ sumunits   : logi FALSE
##  $unitvar : chr "ESTN_UNIT" ##$ unitvars   : chr "ESTN_UNIT"
##  $strata : logi FALSE ##$ strtype    : chr "POST"
##  $stratalut :Classes 'data.table' and 'data.frame': 2 obs. of 5 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr [1:2] "ESTN_UNIT" "ONESTRAT" ##$ strvar     : chr "ONESTRAT"
##  $strwtvar : chr "strwt" ##$ plotsampcnt: NULL
##  $getprop : logi TRUE ##$ unitarea   :Classes 'data.table' and 'data.frame':    2 obs. of  2 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "ESTN_UNIT"
##  $areavar : chr "ACRES" ##$ areaunits  : chr "acres"

#### Population Example 4: Population data setup for plot-level data, with percent by domain as separate columns

These population data are used in: Example 9.

View Example

Here, we set up population data for both times, and population data for transitions.

Let’s first take a look at the first six rows of the example dataset, including 133 plot records.

head(icepctcover)
output
##        plot_id Change Tree11 Shrub11 OtherVegetation11 Barren11 Impervious11
## 1 5.540684e+12      0      0       0                 0        0            0
## 2 5.540696e+12      0      0       0                60        0           40
## 3 5.540708e+12      0      0      20                60       20            0
## 4 5.540720e+12      0      0       0                 0        0            0
## 5 5.540732e+12      0      0       0                 0        0            0
## 6 5.540744e+12      0      0       0                 0        0            0
##   Water11 Tree14 Shrub14 OtherVegetation14 Barren14 Impervious14 Water14
## 1     100      0       0                 0        0            0     100
## 2       0      0       0                60        0           40       0
## 3       0      0      20                60       20            0       0
## 4     100      0       0                 0      100            0       0
## 5     100      0       0                 0      100            0       0
## 6     100      0       0                 0        0            0     100
##   Veg.NonVeg
## 1          0
## 2          0
## 3          0
## 4          0
## 5          0
## 6          0
dim(icepctcover)
output
## [1] 133  15

Then, rename columns for Time 1 cover (names11) and Time 2 cover (names14)

names11 <- names(icepctcover)[endsWith(names(icepctcover), "11")]
names14 <- names(icepctcover)[endsWith(names(icepctcover), "14")]
names11
output
## [1] "Tree11"            "Shrub11"           "OtherVegetation11"
## [4] "Barren11"          "Impervious11"      "Water11"
names14
output
## [1] "Tree14"            "Shrub14"           "OtherVegetation14"
## [4] "Barren14"          "Impervious14"      "Water14"

Population Data for Time 1 (2011)

Now, we need to create a new set of population data define the names of the columns to estimate (i.e., names11). Remember to add unitarea if you want to generate estimates of area.

PBpctpop11 <- modPBpop(pltpct = icepctcover,
pltpctvars = names11,
unitarea = sum(unitarea$ACRES)) Let’s look at the contents of the output list. str(PBpctpop11, max.level = 1) output ## List of 19 ##$ PBx        :Classes 'data.table' and 'data.frame':    798 obs. of  3 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "plot_id"
##  $pltassgnx :Classes 'data.table' and 'data.frame': 133 obs. of 3 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr "plot_id" ##$ plotid     : chr "plot_id"
##  $pntid : NULL ##$ pltassgnid : chr "plot_id"
##  $sumunits : logi FALSE ##$ unitvar    : chr "ONEUNIT"
##  $unitvars : chr "ONEUNIT" ##$ strata     : logi FALSE
##  $strtype : chr "POST" ##$ stratalut  :Classes 'data.table' and 'data.frame':    1 obs. of  5 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr [1:2] "ONEUNIT" "ONESTRAT"
##  $strvar : chr "ONESTRAT" ##$ strwtvar   : chr "strwt"
##  $plotsampcnt: NULL ##$ getprop    : logi FALSE
##  $unitarea :Classes 'data.table' and 'data.frame': 1 obs. of 2 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr "ONEUNIT" ##$ areavar    : chr "AREA"
##  $areaunits : chr "acres" ##$ rowvar     : chr "variable"

Population Data for Time 2 (2014)

For 2014, we need to create a new population data set with the names14 columns before calculating estimates.

PBpctpop14 <- modPBpop(pltpct = icepctcover,
pltpctvars = names14,
unitarea = sum(unitarea$ACRES)) Population Data for Transitions Let’s also look at transitions. In the example which uses this population data we will generate estimates of percent land cover change from vegetated to non-vegetated for all land in Davis and Salt Lake Counties, Utah. This transition was recorded in the initial dataset (i.e., Veg.NonVeg). Again, we need to create a new population dataset defining this column of interest. PBpctpop.veg <- modPBpop(pltpct = icepctcover, pltpctvars = "Veg.NonVeg", unitarea = sum(unitarea$ACRES)
)

#### Population Example 5: Population data with post-stratification for transition estimates

These population data are used in Example 10.

View Example

Our final population dataset for this vignette adds post-stratification for transition estimates.

Let’s add post-stratification to our transition estimates from Time 1 to Time 2. Again, we need to create a new population dataset with information for post-stratification, including strata pixel counts and plot-level strata assignments. This information is provided with FIESTA’s external data.

## Plot-level assignments
head(iceplt)
output
##        plot_id ESTN_UNIT STRATUMCD LON_PUBLIC LAT_PUBLIC
## 1 5.540684e+12        11         2  -112.3625   40.96010
## 2 5.540696e+12        11         2  -112.0733   41.14085
## 3 5.540708e+12        11         2  -111.8916   41.00477
## 4 5.540720e+12        11         2  -112.0735   40.96141
## 5 5.540732e+12        11         2  -112.1912   41.00827
## 6 5.540744e+12        11         2  -112.2082   40.85725
## Strata weights by estimation unit
head(read.csv(strlutfn))
output
##   ESTN_UNIT STRATUMCD P1POINTCNT
## 1        11         2      26266
## 2        35         1       9050
## 3        35         2      24432

Here we use the strata_opts parameter to calculate the strata weights from the pixel count information in strlutfn

PBpopareaPS <- modPBpop(pntdat = icepnt,
pltassgn = iceplt,
pltassgnid = "plot_id",
pntid = "dot_cnt",
strata = TRUE,
stratalut = strlutfn,
strvar = "STRATUMCD",
strata_opts = list(getwt=TRUE,
getwtvar="P1POINTCNT"),
unitarea = sum(unitarea$ACRES)) Let’s look at the contents of the output list. str(PBpopareaPS, max.level = 1) output ## List of 18 ##$ PBx        :Classes 'data.table' and 'data.frame':    1305 obs. of  11 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr [1:2] "plot_id" "dot_cnt"
##  $pltassgnx :Classes 'data.table' and 'data.frame': 133 obs. of 3 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr "plot_id" ##$ plotid     : chr "plot_id"
##  $pntid : chr "dot_cnt" ##$ pltassgnid : chr "plot_id"
##  $sumunits : logi FALSE ##$ unitvar    : chr "ONEUNIT"
##  $unitvars : chr "ONEUNIT" ##$ strata     : logi TRUE
##  $strtype : chr "POST" ##$ stratalut  :Classes 'data.table' and 'data.frame':    2 obs. of  6 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr [1:2] "ONEUNIT" "STRATUMCD"
##  $strvar : chr "STRATUMCD" ##$ strwtvar   : chr "strwt"
##  $plotsampcnt: NULL ##$ getprop    : logi TRUE
##  $unitarea :Classes 'data.table' and 'data.frame': 1 obs. of 2 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr "ONEUNIT" ##$ areavar    : chr "AREA"
##  $areaunits : chr "acres" And look more closely at the resulting stratalut. PBpopareaPS$stratalut
output
## Key: <ONEUNIT, STRATUMCD>
##    ONEUNIT STRATUMCD P1POINTCNT n.total n.strata     strwt
##     <fctr>    <char>      <num>   <int>    <int>     <num>
## 1:       1         1       9050     133       18 0.1514695
## 2:       1         2      50698     133      115 0.8485305

Now, of course we can make the same population dataset without strata. We do so below.

PBpoparea_nonPS <- modPBpop(pntdat = icepnt,
pltassgn = iceplt,
pltassgnid = "plot_id",
pntid = "dot_cnt",
strata = FALSE,
unitarea = sum(unitarea$ACRES)) ### modPB #### Example 1: Point-level data - Totals View Example In this example, we look at estimating the percent land cover at Time 1 (2011) and land cover at Time 2 (2014) for all land in Davis and Salt Lake Counties, Utah. We will then compare the net change from Time 1 and Time 2. We use population data from Population Example 1. We first estimate the percent land cover at Time 1 (2011) for all land in Davis and Salt Lake Counties, Utah. We will add a lookup table for the rows to get row names. Adding row.add0=TRUE in table_opts list assures that all categories in rowlut are included in the result. We can also add a pretty name to the output names. cover1 <- modPB(PBpopdat = PBpopdat, rowvar = "cover_1", table_opts = list(rowlut = icecover_1, row.add0 = TRUE), title_opts = list(title.rowvar = "Land Cover (2011)")) We can look at the structure of this output with str. Note that again FIESTA outputs a list. str(cover1, max.level = 2) output ## List of 2 ##$ est:'data.frame': 8 obs. of  3 variables:
##   ..$Land Cover (2011) : chr [1:8] "Tree" "Shrub" "OtherVegetation" "Barren" ... ## ..$ Estimate              : chr [1:8] "17.6" "11.5" "24.9" "11.8" ...
##   ..$Percent Sampling Error: chr [1:8] "14.36" "19.6" "11.72" "19.53" ... ##$ raw:List of 8
##   ..$unit_totest:'data.frame': 1 obs. of 15 variables: ## ..$ unit_rowest:'data.frame':  7 obs. of  17 variables:
##   ..$module : chr "PB" ## ..$ esttype    : chr "AREA"
##   ..$PBmethod : chr "HT" ## ..$ strtype    : chr "POST"
##   ..$rowvar : chr "cover_1_nm" ## ..$ pltdom.row :Classes 'data.table' and 'data.frame': 233 obs. of  7 variables:
##   .. ..- attr(*, ".internal.selfref")=<externalptr>
##   .. ..- attr(*, "sorted")= chr "plot_id"

…and the estimates.

str(cover1$est, max.level = 2) output ## 'data.frame': 8 obs. of 3 variables: ##$ Land Cover (2011)     : chr  "Tree" "Shrub" "OtherVegetation" "Barren" ...
##  $Estimate : chr "17.6" "11.5" "24.9" "11.8" ... ##$ Percent Sampling Error: chr  "14.36" "19.6" "11.72" "19.53" ...

The raw list shows more details of the estimates for row totals. See help(modPB) for variable descriptions.

cover1$raw$unit_rowest
output
##   ONEUNIT Land Cover (2011)        phat     phat.var NBRPNTS cover_1        est
## 1       1              Tree 0.176106934 6.395688e-04     166     110 17.6106934
## 2       1             Shrub 0.115288221 5.104315e-04     122     130 11.5288221
## 3       1   OtherVegetation 0.248955723 8.518918e-04     426     140 24.8955723
## 4       1            Barren 0.117794486 5.292122e-04     233     210 11.7794486
## 5       1        Impervious 0.107602339 4.350856e-04     124     220 10.7602339
## 6       1             Water 0.231244779 1.280138e-03     232     310 23.1244779
## 7       1   Uninterpretable 0.003007519 9.045169e-06       2     999  0.3007519
##       est.var    est.se    est.cv       pse  CI99left CI99right  CI95left
## 1  6.39568847 2.5289698 0.1436042  14.36042 11.096499 24.124888 12.654004
## 2  5.10431543 2.2592732 0.1959674  19.59674  5.709320 17.348324  7.100728
## 3  8.51891761 2.9187185 0.1172385  11.72385 17.377452 32.413693 19.174989
## 4  5.29212198 2.3004613 0.1952945  19.52945  5.853853 17.705044  7.270627
## 5  4.35085587 2.0858705 0.1938499  19.38499  5.387387 16.133080  6.672003
## 6 12.80138483 3.5779023 0.1547236  15.47236 13.908412 32.340543 16.111918
## 7  0.09045169 0.3007519 1.0000000 100.00000  0.000000  1.075437  0.000000
##    CI95right     CI68left CI68right
## 1 22.5673832 15.095739403 20.125647
## 2 15.9569162  9.282070002 13.775574
## 3 30.6161554 21.993029656 27.798115
## 4 16.2882698  9.491736793 14.067160
## 5 14.8484650  8.685923525 12.834544
## 6 30.1370375 19.566404719 26.682551
## 7  0.8902147  0.001666802  0.599837

We can also look at the domain-level data used for generating the estimates, with proportion of points by category.

head(cover1$raw$pltdom.row)
output
## Key: <plot_id>
##    ONEUNIT ONESTRAT        plot_id      cover_1_nm nbrpts.pltdom PtsPerPlot
##     <fctr>    <num>         <char>          <char>         <int>      <int>
## 1:       1        1 11940039010690           Water             5          5
## 2:       1        1 11940051010690 OtherVegetation             3          5
## 3:       1        1 11940051010690           Shrub             1          5
## 4:       1        1 11940051010690          Barren             1          5
## 5:       1        1 11940063010690           Water             5          5
## 6:       1        1 11940075010690 OtherVegetation             2          5
##    p.pltdom
##       <num>
## 1:      1.0
## 2:      0.6
## 3:      0.2
## 4:      0.2
## 5:      1.0
## 6:      0.4

Note: An Uninterpretable class is included in the previous table. To remove, add nonsamp.pntfilter. Let’s return a list of titles that are generated automatically.

cover1 <- modPB(PBpopdat = PBpopdat,
rowvar = "cover_1",
nonsamp.pntfilter = "cover_1 != 999", # added filter
table_opts = list(rowlut = icecover_1),
title_opts = list(title.rowvar = "Land Cover (2011)"),
returntitle = TRUE)
cover1$est output ## Land Cover (2011) Estimate Percent Sampling Error ## 1 Tree 17.9 14.43 ## 2 Shrub 11.5 19.6 ## 3 OtherVegetation 24.9 11.72 ## 4 Barren 11.8 19.53 ## 5 Impervious 10.8 19.38 ## 6 Water 23.1 15.47 ## 7 Total Total Total #### Example 2: Point-level data - Totals - Time 1 View Example In this example, we estimate area, in acres, of land cover at Time 1 (2011) for all land in Davis and Salt Lake Counties, Utah. Note: since we are adding area, we require a new set of population data to include area information. This new population data was generated in Population Example 2 Now, let’s get the estimates, adding tabtype = "AREA", to indicate we want area estimates. cover1.area <- modPB(PBpopdat = PBpoparea, tabtype = "AREA", rowvar = "cover_1", nonsamp.pntfilter = "cover_1 != 999", table_opts = list(rowlut = icecover_1), title_opts = list(title.rowvar = "Land Cover (2011)")) Again, we can look at the contents of the output list. str(cover1.area, max.level = 1) output ## List of 2 ##$ est:'data.frame': 7 obs. of  3 variables:
##  $raw:List of 9 And the estimates: ## Estimate and percent sampling error of estimate cover1.area$est
output
##   Land Cover (2011) Estimate Percent Sampling Error
## 1              Tree 165240.8                  14.43
## 2             Shrub 106358.3                   19.6
## 3   OtherVegetation 229672.4                  11.72
## 4            Barren 108670.5                  19.53
## 5        Impervious  99267.8                  19.38
## 6             Water 213333.3                  15.47
## 7             Total    Total                  Total

We can now use the PBpoparea set of population data to run percent estimates as well. Let’s save the data to the outfolder and return titles as well. Note: Saving data adds a new folder in outfolder that includes rawdata files.

cover1.pct <- modPB(PBpopdat = PBpoparea,
tabtype = "PCT",
rowvar = "cover_1",
nonsamp.pntfilter = "cover_1 != 999",
table_opts = list(rowlut = icecover_1),
title_opts = list(title.rowvar = "Land Cover (2011)"),
returntitle = TRUE,
savedata = TRUE,
savedata_opts = list(outfolder = outfolder))

Again, we can look at the contents of the output list. The output now includes titlelst, a list of associated titles.

str(cover1.pct, max.level = 1)
output
## List of 3
##  $est :'data.frame': 7 obs. of 3 variables: ##$ titlelst:List of 9
##  $raw :List of 9 The estimates: ## Estimate and percent sampling error of estimate cover1.pct$est
output
##   Land Cover (2011) Estimate Percent Sampling Error
## 1              Tree     17.9                  14.43
## 2             Shrub     11.5                   19.6
## 3   OtherVegetation     24.9                  11.72
## 4            Barren     11.8                  19.53
## 5        Impervious     10.8                  19.38
## 6             Water     23.1                  15.47
## 7             Total    Total                  Total

And titles:

## Estimate and percent sampling error of estimate
cover1.pct$titlelst output ##$title.estpse
## [1] "Estimated percent, in acres, and percent sampling error all lands by land cover (2011)"
##
## $title.unitvar ## [1] "ONEUNIT" ## ##$title.ref
## [1] ""
##
## $outfn.estpse ## [1] "photo_nratio_pct_cover_1_nm_allland" ## ##$outfn.rawdat
## [1] "photo_nratio_pct_cover_1_nm_allland_rawdata"
##
## $outfn.param ## [1] "photo_nratio_pct_cover_1_nm_allland_parameters" ## ##$title.rowvar
## [1] "Land Cover (2011)"
##
## $title.row ## [1] "Estimated percent, in acres, all lands by land cover (2011)" ## ##$title.unitsn
## [1] "acres"

#### Example 3: Point-level data - Totals - Time 2

View Example

Now, let’s generate estimates of percent land cover at Time 2 (2014) for all land in Davis and Salt Lake Counties, Utah. Then we can compare the estimates. We can use the same population data for this analysis. This example uses population data from Population Example 2.

cover2 <- modPB(PBpopdat = PBpoparea,
rowvar = "cover_2",
nonsamp.pntfilter = "cover_1 != 999",
table_opts = list(rowlut = icecover_2),
title_opts = list(title.rowvar = "Land Cover (2014)"),
returntitle = TRUE)

Again, we can look at the contents of the output list. The output now includes titlelst, a list of associated titles.

str(cover2, max.level = 1)
output
## List of 3
##  $est :'data.frame': 7 obs. of 3 variables: ##$ titlelst:List of 9
##  $raw :List of 9 And the estimates: ## Estimate and percent sampling error of estimate cover2$est
output
##   Land Cover (2014) Estimate Percent Sampling Error
## 1              Tree     17.8                  14.53
## 2             Shrub     10.8                  19.96
## 3   OtherVegetation     25.7                  11.58
## 4            Barren     16.9                  16.36
## 5        Impervious     11.5                  18.44
## 6             Water     17.2                  18.71
## 7             Total    Total                  Total

Now we can compare the estimates from Time 2 with estimates from Time 1 and look at net change. We will use the raw data with numeric values.

netchg <- data.frame(Estimate1 = cover1$raw$unit_rowest$est, Estimate2 = cover2$raw$unit_rowest$est,
NetChange.1to2 = cover1$raw$unit_rowest$est - cover2$raw$unit_rowest$est)
netchg
output
##   Estimate1 Estimate2 NetChange.1to2
## 1  17.91145  17.81119      0.1002506
## 2  11.52882  10.81036      0.7184628
## 3  24.89557  25.74770     -0.8521303
## 4  11.77945  16.89223     -5.1127820
## 5  10.76023  11.49541     -0.7351713
## 6  23.12448  17.24311      5.8813701

Now, let’s create a barplot to compare net change. First, we need to set up a data frame with estimates and standard errors.

tabvars <- c("est", "est.se")
tab1 <- cover1$raw$unit_rowest[, c("cover_1", cover1$titlelst$title.rowvar, tabvars)]
data.table::setnames(tab1, tabvars, paste0(tabvars, ".1"))

tab2 <- cover2$raw$unit_rowest[, c("cover_2", cover2$titlelst$title.rowvar, tabvars)]
data.table::setnames(tab2, tabvars, paste0(tabvars, ".2"))

tabx <- merge(tab1, tab2, by.x="cover_1", by.y="cover_2")
tabx
output
##   cover_1 Land Cover (2011)    est.1 est.se.1 Land Cover (2014)    est.2
## 1     110              Tree 17.91145 2.584435              Tree 17.81119
## 2     130             Shrub 11.52882 2.259273             Shrub 10.81036
## 3     140   OtherVegetation 24.89557 2.918718   OtherVegetation 25.74770
## 4     210            Barren 11.77945 2.300461            Barren 16.89223
## 5     220        Impervious 10.76023 2.085871        Impervious 11.49541
## 6     310             Water 23.12448 3.577902             Water 17.24311
##   est.se.2
## 1 2.587722
## 2 2.158107
## 3 2.982247
## 4 2.763902
## 5 2.119935
## 6 3.225573

Next, the barplot.

sevar <- names(tabx)[grepl("est.se", names(tabx))]
yvar <- names(tabx)[grepl("est.", names(tabx)) & !names(tabx) %in% sevar]
xvar <- cover1$titlelst$title.rowvar

datBarplot(tabx,
yvar = yvar,
xvar = xvar,
errbars = TRUE,
sevar = sevar,
ylabel = "Percent",
args.legend = list(x = "topleft",
bty = "n",
cex = .8,
legend = c("2011", "2014")),
main = substr(cover1$titlelst$title.row,
1,
nchar(cover1$titlelst$title.row)-7))
plot

#### Example 4: EPoint-level data - Totals - Agent of Change

View Example

In this example, we generate estimates of percent change by agent in Davis and Salt Lake Counties, Utah. Here, we use the same population data. We also add the lookup table with agent of change code names. This example uses population data from Population Example 2.

chg_ag <- modPB(PBpopdat = PBpoparea,
rowvar = "chg_ag_2",
table_opts = list(rowlut = icechg_ag),
title_opts = list(title.rowvar = "Agent of Change"),
returntitle=TRUE)

Let’s again look at the contents of the output list.

str(chg_ag, max.level = 1)
output
## List of 3
##  $est :'data.frame': 6 obs. of 3 variables: ##$ titlelst:List of 9
##  $raw :List of 9 And the estimates: ## Estimate and percent sampling error of estimate chg_ag$est
output
##                 Agent of Change Estimate Percent Sampling Error
## 1                     No Change       91                   2.49
## 2                   Development      3.9                  40.78
## 3 Removal or Loss of Vegetation      0.1                  68.17
## 4           Stress or Mortality      0.1                    100
## 5               Expected Change        5                  34.25
## 6                         Total    Total                  Total

Now, let’s get area estimates. Notice, we can change the resulting area units to metric units (i.e., hectares).

chg_ag.area <- modPB(PBpopdat = PBpoparea,
tabtype = "AREA",
rowvar = "chg_ag_2",
table_opts = list(rowlut = icechg_ag, metric=TRUE),
title_opts = list(title.rowvar = "Agent of Change"),
returntitle=TRUE)

Again, we can look at the contents of the output list.

str(chg_ag.area, max.level = 1)
output
## List of 3
##  $est :'data.frame': 6 obs. of 3 variables: ##$ titlelst:List of 9
##  $raw :List of 9 And the estimates: ## Estimate and percent sampling error of estimate chg_ag.area$est
output
##                 Agent of Change Estimate Percent Sampling Error
## 1                     No Change   339593                   2.49
## 2                   Development  14409.6                  40.78
## 3 Removal or Loss of Vegetation      499                  68.17
## 4           Stress or Mortality    187.1                    100
## 5               Expected Change  18651.4                  34.25
## 6                         Total    Total                  Total

The resulting area units are identified in the raw data.

chg_ag.area$raw$areaunits
output
## [1] "hectares"

#### Example 5: Point-level data - Totals - Agent of Change - Filters

View Example

We can also apply filters to subset the resulting table. This filter subsets the plots that had observed change. Filters do not affect the population data, thus, we will continue using the same PBpoparea dataset from Population Example 2.

Here, we generate estimates of percent land with observed change by agent of change in Davis and Salt Lake Counties, Utah.

# Add a landarea filter to subset dataset to only plots with observed change.
landarea.filter <- "change_1_2 == 1"

chg_ag.plts <- modPB(PBpopdat = PBpoparea,
rowvar = "chg_ag_2",
table_opts = list(rowlut = icechg_ag),
title_opts = list(title.rowvar = "Agent of Change"),
landarea = "CHANGE",
landarea.filter = landarea.filter,
returntitle = TRUE)

The resulting estimates…

## Estimate and percent sampling error of estimate
chg_ag.plts$est output ## Agent of Change Estimate Percent Sampling Error ## 1 No Change 6.3 29.8 ## 2 Development 3.9 40.78 ## 3 Removal or Loss of Vegetation 0.1 68.17 ## 4 Stress or Mortality 0.1 100 ## 5 Expected Change 1.7 50.46 ## 6 Total Total Total Notice, the estimate titles reflect this filter. ## Estimate and percent sampling error of estimate chg_ag.plts$titlelst
output
## $title.estpse ## [1] "Estimated percent, in acres, and percent sampling error land with observed change by agent of change" ## ##$title.unitvar
## [1] "ONEUNIT"
##
## $title.ref ## [1] "" ## ##$outfn.estpse
## [1] "photo_nratio_pct_chg_ag_2_nm_change"
##
## $outfn.rawdat ## [1] "photo_nratio_pct_chg_ag_2_nm_change_rawdata" ## ##$outfn.param
## [1] "photo_nratio_pct_chg_ag_2_nm_change_parameters"
##
## $title.rowvar ## [1] "Agent of Change" ## ##$title.row
## [1] "Estimated percent, in acres, land with observed change by agent of change"
##
## $title.unitsn ## [1] "acres" Now, let’s add a pntfilter to only look at points that changed. # Percent land changed by agent of change in Davis and Salt Lake Counties, UT pntfilter <- "chg_ag_2 > 0" chg_ag.pnts <- modPB(PBpopdat = PBpoparea, rowvar = "chg_ag_2", table_opts = list(rowlut = icechg_ag), title_opts = list(title.rowvar = "Agent of Change", title.filter = "observed changed"), pntfilter = pntfilter, returntitle = TRUE) We can now compare the estimates and percent sampling errors. # All land chg_ag$titlelst$title.estpse output ## [1] "Estimated percent, in acres, and percent sampling error all lands by agent of change" chg_ag$est
output
##                 Agent of Change Estimate Percent Sampling Error
## 1                     No Change       91                   2.49
## 2                   Development      3.9                  40.78
## 3 Removal or Loss of Vegetation      0.1                  68.17
## 4           Stress or Mortality      0.1                    100
## 5               Expected Change        5                  34.25
## 6                         Total    Total                  Total
# Land with observed change
chg_ag.plts$titlelst$title.estpse
output
## [1] "Estimated percent, in acres, and percent sampling error land with observed change by agent of change"
chg_ag.plts$est output ## Agent of Change Estimate Percent Sampling Error ## 1 No Change 6.3 29.8 ## 2 Development 3.9 40.78 ## 3 Removal or Loss of Vegetation 0.1 68.17 ## 4 Stress or Mortality 0.1 100 ## 5 Expected Change 1.7 50.46 ## 6 Total Total Total # Estimated change chg_ag.pnts$titlelst$title.estpse output ## [1] "Estimated percent, in acres, and percent sampling error all lands by agent of change (observed changed)" chg_ag.pnts$est
output
##                 Agent of Change Estimate Percent Sampling Error
## 1                   Development      5.2                  36.93
## 2 Removal or Loss of Vegetation      1.5                  68.82
## 3           Stress or Mortality      0.8                    100
## 4               Expected Change      7.5                  30.53
## 5                         Total    Total                  Total

Let’s create a barplot of estimated change by agent with the datBarplot() function from FIESTA.

datBarplot(chg_ag.pnts$raw$unit_rowest,
xvar = "Agent of Change",
yvar = "est",
errbars = TRUE,
sevar = "est.se",
ylab = "Percent",
main = chg_ag.pnts$titlelst$title.row)
plot

Now, let’s look at at percent of land changed by agent of change and land cover (2011) in Davis and Salt Lake Counties, Utah.

chg_ag_cover1 <- modPB(PBpopdat = PBpoparea,
rowvar = "chg_ag_2",
colvar = "cover_2",
table_opts = list(rowlut = icechg_ag,
collut = icecover_2),
title_opts = list(title.rowvar = "Change agent",
title.colvar = "Land cover (2011)"),
returntitle = TRUE)

The resulting estimates…

chg_ag_cover1$est output ## Change agent Tree Shrub OtherVegetation Barren Impervious ## 1 No Change 17.8 10.8 22.4 12.7 10.4 ## 2 Development -- -- 1.3 1.5 1.1 ## 3 Removal or Loss of Vegetation -- -- -- 0.1 -- ## 4 Stress or Mortality 0.1 -- -- -- -- ## 5 Expected Change -- 0 2 2.6 -- ## 6 Total 17.8 10.8 25.7 16.9 11.5 ## Water Total ## 1 16.9 91 ## 2 -- 3.9 ## 3 -- 0.1 ## 4 -- 0.1 ## 5 0.4 5 ## 6 17.2 100 And percent sampling error… chg_ag_cover1$pse
output
##                    Change agent  Tree Shrub OtherVegetation Barren Impervious
## 1                     No Change 14.51 20.03           12.47  19.42         20
## 2                   Development    --    --           46.88   45.1      52.66
## 3 Removal or Loss of Vegetation    --    --              --  68.17         --
## 4           Stress or Mortality   100    --              --     --         --
## 5               Expected Change    --   100           54.25  50.69         --
## 6                         Total 14.53 19.96           11.58  16.36      18.44
##   Water Total
## 1 19.16  2.49
## 2    -- 40.78
## 3    -- 68.17
## 4    --   100
## 5 62.79 34.25
## 6 18.71     0

#### Example 6: Point-level data - By Estimation Unit

View Example

In this example, we generate estimates by each estimation unit (i.e, County). We have created the necessary population data with a call to modPBpop() in Population Example 3.

Since we have taken care of our population data, let’s start with area, in acres, of land cover at Time 1 (2011) by County for all land in Davis and Salt Lake Counties, UT

cover1.unit.area <- modPB(PBpopdat = PBpopunit,
tabtype = "AREA",
rowvar = "cover_1",
nonsamp.pntfilter = "cover_1 != 999",
table_opts = list(rowlut=icecover_1),
title_opts = list(title.rowvar="Land Cover (2011)"))
## Estimate and percent sampling error of estimate
cover1.unit.area$est output ## Land Cover (2011) 11 35 Total ## 1 Tree 32348.7 129952.9 165240.8 ## 2 Shrub 12070.4 91757.8 106358.3 ## 3 OtherVegetation 96885.2 132489.3 229672.4 ## 4 Barren 55202 54010.3 108670.5 ## 5 Impervious 13518.9 83551.8 99267.8 ## 6 Water 195540.8 25214.8 213333.3 ## 7 Total 405566 516977 922543 ## Percent sampling error of estimate cover1.unit.area$pse
output
##   Land Cover (2011)    11    35 Total
## 1              Tree 36.79 14.82 14.43
## 2             Shrub 46.69 20.38  19.6
## 3   OtherVegetation 20.53    14 11.72
## 4            Barren 31.41 23.84 19.53
## 5        Impervious 46.15 20.42 19.38
## 6             Water 13.97 41.55 15.47
## 7             Total     0     0     0

If we set sumunits = TRUE, we can generate an estimate of area by county and also sum these estimates to the population. Your resulting estimate is for the entire population, but you can find estimates by county in the raw data tables. Here, we can use the sample population that was created by estimation unit (i.e., county).

cover1.unitsum <- modPB(PBpopdat = PBpopunit,
tabtype = "AREA",
sumunits = TRUE,
rowvar = "cover_1",
nonsamp.pntfilter = "cover_1 != 999",
table_opts = list(rowlut=icecover_1),
title_opts = list(title.rowvar="Land Cover (2011)"))

The resulting estimate is for the total population.

## Estimate and percent sampling error of estimate
cover1.unitsum$est output ## Land Cover (2011) Estimate Percent Sampling Error ## 1 Tree 162301.7 13.95 ## 2 Shrub 103828.2 18.81 ## 3 OtherVegetation 229374.6 11.86 ## 4 Barren 109212.3 19.77 ## 5 Impervious 97070.7 18.72 ## 6 Water 220755.5 13.26 ## 7 Total 922543 0 And we can look at the structure of the raw output. str(cover1.unitsum$raw, max.level = 1)
output
## List of 11
##  $unit_totest:'data.frame': 2 obs. of 17 variables: ##$ totest     :'data.frame': 1 obs. of  13 variables:
##  $unit_rowest:'data.frame': 12 obs. of 19 variables: ##$ rowest     :'data.frame': 6 obs. of  13 variables:
##  $module : chr "PB" ##$ esttype    : chr "AREA"
##  $PBmethod : chr "HT" ##$ strtype    : chr "POST"
##  $rowvar : chr "cover_1_nm" ##$ pltdom.row :Classes 'data.table' and 'data.frame':    232 obs. of  7 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "plot_id"
##  $areaunits : chr "acres" Now, let’s look at the raw data output. There are data frames by unit (unit_totest; unit_rowest) and two additional data frames for the total population (totest; rowest). ## Estimate and percent sampling error of estimate cover1.unitsum$raw$unit_rowest output ## ESTN_UNIT Land Cover (2011) phat phat.var NBRPNTS cover_1 ACRES ## 1 11 Tree 0.07976190 0.0008612400 65 110 405566 ## 2 11 Shrub 0.02976190 0.0001931303 11 130 405566 ## 3 11 OtherVegetation 0.23888889 0.0024050826 146 140 405566 ## 4 11 Barren 0.13611111 0.0018278219 63 210 405566 ## 5 11 Impervious 0.03333333 0.0002366522 20 220 405566 ## 6 11 Water 0.48214286 0.0045396568 135 310 405566 ## 7 35 Tree 0.25137085 0.0013873505 101 110 516977 ## 8 35 Shrub 0.17748918 0.0013087421 111 130 516977 ## 9 35 OtherVegetation 0.25627706 0.0012881673 280 140 516977 ## 10 35 Barren 0.10447330 0.0006200796 170 210 516977 ## 11 35 Impervious 0.16161616 0.0010895313 104 220 516977 ## 12 35 Water 0.04877345 0.0004106291 97 310 516977 ## AREAUSED est est.var est.se est.cv pse CI99left ## 1 405566 32348.72 141660003 11902.101 0.3679312 36.79312 1690.937 ## 2 405566 12070.42 31766799 5636.204 0.4669436 46.69436 0.000 ## 3 405566 96885.21 395597073 19889.622 0.2052906 20.52906 45652.939 ## 4 405566 55202.04 300647051 17339.177 0.3141039 31.41039 10539.279 ## 5 405566 13518.87 38925455 6239.027 0.4615052 46.15052 0.000 ## 6 405566 195540.75 746699907 27325.810 0.1397448 13.97448 125154.127 ## 7 516977 129952.95 370790531 19255.922 0.1481761 14.81761 80352.981 ## 8 516977 91757.82 349781255 18702.440 0.2038239 20.38239 43583.530 ## 9 516977 132489.34 344282312 18554.846 0.1400478 14.00478 84695.228 ## 10 516977 54010.30 165725723 12873.450 0.2383518 23.83518 20850.485 ## 11 516977 83551.84 291193820 17064.402 0.2042373 20.42373 39596.851 ## 12 516977 25214.75 109746874 10476.014 0.4154716 41.54716 0.000 ## CI99right CI95left CI95right CI68left CI68right ## 1 63006.50 9021.028 55676.41 20512.579 44184.85 ## 2 26588.32 1023.659 23117.17 6465.449 17675.38 ## 3 148117.48 57902.268 135868.15 77105.819 116664.60 ## 4 99864.80 21217.877 89186.20 37958.958 72445.12 ## 5 29589.53 1290.599 25747.13 7314.417 19723.32 ## 6 265927.37 141983.146 249098.35 168366.383 222715.12 ## 7 179552.92 92212.035 167693.86 110803.745 149102.15 ## 8 139932.12 55101.714 128413.93 73159.034 110356.61 ## 9 180283.46 96122.514 168856.17 114037.331 150941.36 ## 10 87170.11 28778.797 79241.79 41208.191 66812.40 ## 11 127506.83 50106.225 116997.45 66582.009 100521.67 ## 12 52199.18 4682.141 45747.36 14796.796 35632.71 ## Estimate and percent sampling error of estimate cover1.unitsum$raw$rowest output ## Land Cover (2011) est est.var AREAUSED est.se est.cv pse ## 1 Tree 162301.67 512450534 922543 22637.37 0.1394771 13.94771 ## 2 Shrub 103828.24 381548054 922543 19533.26 0.1881305 18.81305 ## 3 OtherVegetation 229374.55 739879386 922543 27200.72 0.1185865 11.85865 ## 4 Barren 109212.33 466372774 922543 21595.67 0.1977402 19.77402 ## 5 Impervious 97070.71 330119275 922543 18169.18 0.1871747 18.71747 ## 6 Water 220755.50 856446781 922543 29265.11 0.1325680 13.25680 ## CI99left CI99right CI95left CI95right CI68left CI68right ## 1 103991.66 220611.7 117933.23 206670.1 139789.75 184813.6 ## 2 53513.91 154142.6 65543.76 142112.7 84403.24 123253.2 ## 3 159310.13 299439.0 176062.12 282687.0 202324.58 256424.5 ## 4 53585.59 164839.1 66885.61 151539.1 87736.35 130688.3 ## 5 50269.99 143871.4 61459.76 132681.7 79002.22 115139.2 ## 6 145373.57 296137.4 163396.94 278114.1 191652.58 249858.4 #### Example 7: Point-level transition data (T1 Cover - T2 Cover) View Example In this example, we look at the transition data at the point level, giving an estimate of what each category transitioned to. Let’s look at a table of the percent land cover at Time 1 (2011) by percent land cover at Time 2 (2014) for all and in Davis and Salt Lake Counties, Utah. Here, we use the PBpoparea population dataset from Population Example 2 as the population dataset. cover12 <- modPB(PBpopdat = PBpoparea, rowvar = "cover_1", colvar = "cover_2", nonsamp.pntfilter = "cover_1 != 999", table_opts = list(rowlut = icecover_1, collut = icecover_2), title_opts = list(title.rowvar = "Land Cover (2011)", title.colvar = "Land Cover (2014)"), returntitle = TRUE) Now, look at the estimates. cover12$est
output
##   Land Cover (2011) Tree Shrub OtherVegetation Barren Impervious Water Total
## 1              Tree 17.8    --             0.1      0          0    --  17.9
## 2             Shrub   --  10.8             0.5    0.3         --    --  11.5
## 3   OtherVegetation   --     0            23.1      1        0.4   0.4  24.9
## 4            Barren   --    --             0.4     11        0.4    --  11.8
## 5        Impervious   --    --              --      0       10.7    --  10.8
## 6             Water   --    --             1.7    4.6         --  16.9  23.1
## 7             Total 17.8  10.8            25.7   16.9       11.5  17.2   100

… and percent standard error.

cover12$pse output ## Land Cover (2011) Tree Shrub OtherVegetation Barren Impervious Water Total ## 1 Tree 14.53 -- 100 100 100 -- 14.43 ## 2 Shrub -- 20.03 100 100 -- -- 19.6 ## 3 OtherVegetation -- 100 12.02 44.98 71.17 62.79 11.72 ## 4 Barren -- -- 65.47 20.16 58.14 -- 19.53 ## 5 Impervious -- -- -- 100 19.41 -- 19.38 ## 6 Water -- -- 53.25 39.46 -- 19.16 15.47 ## 7 Total 14.53 19.96 11.58 16.36 18.44 18.71 0 We can also look at the summed proportions for each transition (i.e, row and column). head(cover12$raw$pltdom.grp) output ## Key: <plot_id> ## ONEUNIT ONESTRAT plot_id cover_1_nm cover_2_nm ## <fctr> <num> <char> <char> <char> ## 1: 1 1 11940039010690 Water Water ## 2: 1 1 11940051010690 OtherVegetation OtherVegetation ## 3: 1 1 11940051010690 Shrub Shrub ## 4: 1 1 11940051010690 Barren Barren ## 5: 1 1 11940063010690 Water Water ## 6: 1 1 11940075010690 OtherVegetation OtherVegetation ## nbrpts.pltdom PtsPerPlot p.pltdom ## <int> <int> <num> ## 1: 5 5 1.0 ## 2: 3 5 0.6 ## 3: 1 5 0.2 ## 4: 1 5 0.2 ## 5: 5 5 1.0 ## 6: 2 5 0.4 … and the raw data estimates for each transition. head(cover12$raw$unit_grpest) output ## ONEUNIT Land Cover (2011) Land Cover (2014) phat phat.var cover_2 ## 1 1 Tree Tree 0.1781119465 6.696306e-04 110 ## 2 1 Tree OtherVegetation 0.0005012531 2.512547e-07 140 ## 3 1 Tree Barren 0.0001670844 2.791719e-08 210 ## 4 1 Tree Impervious 0.0003341688 1.116688e-07 220 ## 5 1 Shrub Shrub 0.1077694236 4.661765e-04 130 ## 6 1 Shrub OtherVegetation 0.0048454470 2.347836e-05 140 ## cover_1 est est.var est.se est.cv pse CI99left ## 1 110 17.81119465 6.6963058389 2.58772213 0.1452863 14.52863 11.145664 ## 2 110 0.05012531 0.0025125470 0.05012531 1.0000000 100.00000 0.000000 ## 3 110 0.01670844 0.0002791719 0.01670844 1.0000000 100.00000 0.000000 ## 4 110 0.03341688 0.0011166876 0.03341688 1.0000000 100.00000 0.000000 ## 5 130 10.77694236 4.6617645351 2.15911198 0.2003455 20.03455 5.215438 ## 6 130 0.48454470 0.2347835615 0.48454470 1.0000000 100.00000 0.000000 ## CI99right CI95left CI95right CI68left CI68right ## 1 24.47672515 12.739352 22.88303684 1.523781e+01 20.38457533 ## 2 0.17923956 0.000000 0.14836912 2.778003e-04 0.09997283 ## 3 0.05974652 0.000000 0.04945637 9.260011e-05 0.03332428 ## 4 0.11949304 0.000000 0.09891275 1.852002e-04 0.06664855 ## 5 16.33844626 6.545161 15.00872407 8.629796e+00 12.92408828 ## 6 1.73264912 0.000000 1.43423485 2.685403e-03 0.96640399 We also can see estimates for transition (Time 1 by Time 2). cover12$raw$unit.grpest output ## NULL We can do the same for area estimates by just adding the tabtype='AREA' parameter. Area, in acres, of land cover at Time 1 (2011) by land cover at Time 2 (2014) for all land in Davis and Salt Lake Counties, Utah. cover12.area <- modPB(PBpopdat = PBpoparea, tabtype = "AREA", rowvar = "cover_1", colvar = "cover_2", nonsamp.pntfilter="cover_1 != 999", table_opts = list(rowlut = icecover_1, collut = icecover_2), title_opts = list(title.rowvar = "Land Cover (2011)", title.colvar = "Land Cover (2014)"), returntitle = TRUE) Let’s check to make sure the percent standard errors (pse) match. head(cover12$pse)
output
##   Land Cover (2011)  Tree Shrub OtherVegetation Barren Impervious Water Total
## 1              Tree 14.53    --             100    100        100    -- 14.43
## 2             Shrub    -- 20.03             100    100         --    --  19.6
## 3   OtherVegetation    --   100           12.02  44.98      71.17 62.79 11.72
## 4            Barren    --    --           65.47  20.16      58.14    -- 19.53
## 5        Impervious    --    --              --    100      19.41    -- 19.38
## 6             Water    --    --           53.25  39.46         -- 19.16 15.47
head(cover12.area$pse) output ## Land Cover (2011) Tree Shrub OtherVegetation Barren Impervious Water Total ## 1 Tree 14.53 -- 100 100 100 -- 14.43 ## 2 Shrub -- 20.03 100 100 -- -- 19.6 ## 3 OtherVegetation -- 100 12.02 44.98 71.17 62.79 11.72 ## 4 Barren -- -- 65.47 20.16 58.14 -- 19.53 ## 5 Impervious -- -- -- 100 19.41 -- 19.38 ## 6 Water -- -- 53.25 39.46 -- 19.16 15.47 We can also look at transitions by concatenating the column names. Again, let’s look at the percent land cover at Time 1 (2011) by percent land cover at Time 2 (2014) for all land in Davis and Salt Lake Counties, Utah. First, a quick diversion into creating a new population dataset. First, we merge the point-level data with each lookup table to get class names, then concatenate the Time 1 and Time 2 named columns. Let’s make a copy of the population data and add the new category directly to the PBx data frame (PBpoparea$PBx) so we don’t have to recreate the population data.

PBpoparea2 <- PBpoparea
PBpoparea2$PBx <- merge(PBpoparea2$PBx, icecover_1, by = "cover_1")
PBpoparea2$PBx <- merge(PBpoparea2$PBx, icecover_2, by = "cover_2")
PBpoparea2$PBx$cover_12_nm <- paste(PBpoparea2$PBx$cover_1_nm,
PBpoparea2$PBx$cover_2_nm,
sep = "-")
head(PBpoparea2$PBx) output ## Key: <cover_2> ## cover_2 cover_1 plot_id dot_cnt change_1_2 use_1 use_2 chg_ag_2 ## <int> <int> <num> <int> <int> <int> <int> <int> ## 1: 110 110 5.540756e+12 1 0 400 400 0 ## 2: 110 110 5.540756e+12 26 0 110 110 0 ## 3: 110 110 5.549764e+12 36 0 110 110 0 ## 4: 110 110 5.549836e+12 1 0 110 110 0 ## 5: 110 110 5.549836e+12 26 0 110 110 0 ## 6: 110 110 5.549836e+12 31 0 110 110 0 ## ESTN_UNIT STRATUMCD LON_PUBLIC LAT_PUBLIC cover_1_nm cover_2_nm cover_12_nm ## <int> <int> <num> <num> <char> <char> <char> ## 1: 11 2 -111.8918 40.82525 Tree Tree Tree-Tree ## 2: 11 2 -111.8918 40.82525 Tree Tree Tree-Tree ## 3: 35 2 -112.1810 40.69342 Tree Tree Tree-Tree ## 4: 35 1 -111.7668 40.65109 Tree Tree Tree-Tree ## 5: 35 1 -111.7668 40.65109 Tree Tree Tree-Tree ## 6: 35 1 -111.7668 40.65109 Tree Tree Tree-Tree Next, generate the estimates from the concatenated column (cover_12_nm) in PBpoparea2. cover12nm <- modPB(PBpopdat = PBpoparea2, rowvar = "cover_12_nm", nonsamp.pntfilter = "cover_1 != 999", title_opts = list(title.rowvar = "Land Cover (2011-2014)"), returntitle = TRUE) We can look at the estimates and compare to the method above. You can see that the estimates are the same, just presented in a different format. cover12nm$est
output
##             Land Cover (2011-2014) Estimate Percent Sampling Error
## 1                    Barren-Barren       11                  20.16
## 2                Barren-Impervious      0.4                  58.14
## 3           Barren-OtherVegetation      0.4                  65.47
## 4                Impervious-Barren        0                    100
## 5            Impervious-Impervious     10.7                  19.41
## 6           OtherVegetation-Barren        1                  44.98
## 7       OtherVegetation-Impervious      0.4                  71.17
## 8  OtherVegetation-OtherVegetation     23.1                  12.02
## 9            OtherVegetation-Shrub        0                    100
## 10           OtherVegetation-Water      0.4                  62.79
## 11                    Shrub-Barren      0.3                    100
## 12           Shrub-OtherVegetation      0.5                    100
## 13                     Shrub-Shrub     10.8                  20.03
## 14                     Tree-Barren        0                    100
## 15                 Tree-Impervious        0                    100
## 16            Tree-OtherVegetation      0.1                    100
## 17                       Tree-Tree     17.8                  14.53
## 18                    Water-Barren      4.6                  39.46
## 19           Water-OtherVegetation      1.7                  53.25
## 20                     Water-Water     16.9                  19.16
## 21                           Total    Total                  Total
cover12$est output ## Land Cover (2011) Tree Shrub OtherVegetation Barren Impervious Water Total ## 1 Tree 17.8 -- 0.1 0 0 -- 17.9 ## 2 Shrub -- 10.8 0.5 0.3 -- -- 11.5 ## 3 OtherVegetation -- 0 23.1 1 0.4 0.4 24.9 ## 4 Barren -- -- 0.4 11 0.4 -- 11.8 ## 5 Impervious -- -- -- 0 10.7 -- 10.8 ## 6 Water -- -- 1.7 4.6 -- 16.9 23.1 ## 7 Total 17.8 10.8 25.7 16.9 11.5 17.2 100 We can also subset the output results by adding a pntfilter parameter. Let’s look at the transition data again, except only look at what the vegetation land (cover_1 < 200) at Time 1 transitioned to in Time 2. Remember, this does not affect your population so we can use the same population dataset. We will also add a pretty name to add to the title for the filter (title.filter). cover12.lt200 <- modPB(PBpopdat = PBpoparea, rowvar = "cover_1", colvar = "cover_2", nonsamp.pntfilter = "cover_1 != 999", pntfilter = "cover_1 < 200", table_opts = list(rowlut = icecover_1, collut = icecover_2), title_opts = list(title.rowvar = "Land Cover (2011)", title.colvar = "Land Cover (2014)", title.filter = "Vegetated land"), returntitle = TRUE) We can look at the resulting estimates. You can see that 69.9 percent of the land was vegetated at Time 1 as shown by the overall total of the table. cover12.lt200$est
output
##   Land Cover (2011) Tree Shrub OtherVegetation Barren Impervious Water Total
## 1              Tree 22.2    --             0.1      0          0    --  22.3
## 2             Shrub   --  12.5             0.5    0.3         --    --  13.2
## 3   OtherVegetation   --   0.2              31    1.7        0.8   0.7  34.4
## 4             Total 22.2  12.7            31.5      2        0.9   0.7  69.9

Now, we can look at the titles and see how adding the title.filter is displayed.

cover12.lt200$titlelst output ##$title.est
## [1] "Estimated percent, in acres, all lands by land cover (2011) and land cover (2014) (Vegetated land)"
##
## $title.pse ## [1] "Percent sampling error of estimated percent, in acres, all lands by land cover (2011) and land cover (2014) (Vegetated land)" ## ##$title.unitvar
## [1] "ONEUNIT"
##
## $title.ref ## [1] "" ## ##$outfn.estpse
## [1] "photo_nratio_pct_cover_1_nm_cover_2_nm_allland"
##
## $outfn.rawdat ## [1] "photo_nratio_pct_cover_1_nm_cover_2_nm_allland_rawdata" ## ##$outfn.param
## [1] "photo_nratio_pct_cover_1_nm_cover_2_nm_allland_parameters"
##
## $title.rowvar ## [1] "Land Cover (2011)" ## ##$title.row
## [1] "Estimated percent, in acres, all lands by land cover (2011) (Vegetated land)"
##
## $title.colvar ## [1] "Land Cover (2014)" ## ##$title.col
## [1] "Estimated percent, in acres, all lands by land cover (2014) (Vegetated land)"
##
## $title.unitsn ## [1] "acres" We can also look at the percent gains and losses from the transition data with associated percent sampling errors by just adding the parameter gainloss = TRUE. cover12b <- modPB(PBpopdat = PBpoparea, rowvar = "cover_1", colvar = "cover_2", nonsamp.pntfilter="cover_1 != 999", table_opts = list(rowlut = icecover_1, collut = icecover_2), title_opts = list(title.rowvar = "Land Cover (2011)", title.colvar = "Land Cover (2014"), returntitle = TRUE, gainloss = TRUE) Here, you can see a new data frame is added to the raw data (est.gainloss). str(cover12b$raw, max.level = 1)
output
## List of 15
##  $unit_totest :'data.frame': 1 obs. of 15 variables: ##$ unit_rowest :'data.frame':    6 obs. of  17 variables:
##  $unit_colest :'data.frame': 6 obs. of 17 variables: ##$ unit_grpest :'data.frame':    20 obs. of  18 variables:
##  $module : chr "PB" ##$ esttype     : chr "AREA"
##  $PBmethod : chr "HT" ##$ strtype     : chr "POST"
##  $rowvar : chr "cover_1_nm" ##$ pltdom.row  :Classes 'data.table' and 'data.frame':   232 obs. of  7 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "plot_id"
##  $colvar : chr "cover_2_nm" ##$ pltdom.col  :Classes 'data.table' and 'data.frame':   235 obs. of  7 variables:
##   ..- attr(*, ".internal.selfref")=<externalptr>
##   ..- attr(*, "sorted")= chr "plot_id"
##  $pltdom.grp :Classes 'data.table' and 'data.frame': 258 obs. of 8 variables: ## ..- attr(*, ".internal.selfref")=<externalptr> ## ..- attr(*, "sorted")= chr "plot_id" ##$ areaunits   : chr "acres"
##  $est.gainloss:'data.frame': 6 obs. of 23 variables: Here we see estimates of gains and losses by category. cover12b$raw$est.gainloss output ## ONEUNIT gain.val ## Water 1 Not-Water to Water ## OtherVegetation 1 Not-OtherVegetation to OtherVegetation ## Shrub 1 Not-Shrub to Shrub ## Barren 1 Not-Barren to Barren ## Tree 1 Not-Tree to Tree ## Impervious 1 Not-Impervious to Impervious ## loss.val gain.est gain.se ## Water Water to Not-Water 0.38429407 0.24131456 ## OtherVegetation OtherVegetation to Not-OtherVegetation 2.60651629 1.03970748 ## Shrub Shrub to Not-Shrub 0.03341688 0.03341688 ## Barren Barren to Not-Barren 5.86466165 1.85799417 ## Tree Tree to Not-Tree 0.00000000 0.00000000 ## Impervious Impervious to Not-Impervious 0.75187970 0.38675722 ## loss.est loss.se diff.est diff.se CI99left ## Water 6.26566416 1.98838493 -5.8813701 1.9990174 -11.0304978 ## OtherVegetation 1.75438596 0.64871774 0.8521303 1.2035243 -2.2479428 ## Shrub 0.75187970 0.75187970 -0.7184628 0.7528748 -2.6577398 ## Barren 0.75187970 0.44239370 5.1127820 1.9191564 0.1693626 ## Tree 0.10025063 0.10025063 -0.1002506 0.1002506 -0.3584791 ## Impervious 0.01670844 0.01670844 0.7351713 0.3792919 -0.2418200 ## CI99right gain.CI95left gain.CI95right gain.CI68left ## Water -0.7322424 -0.088673786 0.85726192 0.1443168979 ## OtherVegetation 3.9522034 0.568727083 4.64430550 1.5725709947 ## Shrub 1.2208141 -0.032078997 0.09891275 0.0001852002 ## Barren 10.0562013 2.223059998 9.50626331 4.0169647052 ## Tree 0.1579779 0.000000000 0.00000000 0.0000000000 ## Impervious 1.7121625 -0.006150519 1.50990992 0.3672659346 ## gain.CI68right loss.CI95left loss.CI95right loss.CI68left ## Water 0.62427124 2.36850131 10.16282702 4.288299e+00 ## OtherVegetation 3.64046159 0.48292256 3.02584937 1.109263e+00 ## Shrub 0.06664855 -0.72177743 2.22553683 4.167005e-03 ## Barren 7.71235860 -0.11519602 1.61895542 3.119378e-01 ## Tree 0.00000000 -0.09623699 0.29673824 5.556007e-04 ## Impervious 1.13649346 -0.01603950 0.04945637 9.260011e-05 ## loss.CI68right diff.CI95left diff.CI95right diff.CI68left ## Water 8.24302923 -9.799372265 -1.96336792 -7.8693087 ## OtherVegetation 2.39950844 -1.506733903 3.21099455 -0.3447239 ## Shrub 1.49959239 -2.194070309 0.75714466 -1.4671651 ## Barren 1.19182160 1.351304521 8.87425939 3.2042617 ## Tree 0.19994565 -0.296738244 0.09623699 -0.1999457 ## Impervious 0.03332428 -0.008227219 1.47856974 0.3579814 ## diff.CI68right ## Water -3.8934314469 ## OtherVegetation 2.0489845261 ## Shrub 0.0302394524 ## Barren 7.0213021705 ## Tree -0.0005556007 ## Impervious 1.1123610901 We can also use a bar plot to show the difference in percentage between Time 1 and Time 2 by using the datPBplotchg() from FIESTA. Here, we can easily see the percent gains and percent loss by each category, with confidence intervals. datPBplotchg(cover12b$raw$est.gainloss) plot Let’s look more closely at gain and loss of the OtherVegetation category. ## We will first subset the raw data frame and set to an object estcat <- "OtherVegetation" othveg.gainloss <- cover12b$raw$est.gainloss[row.names(cover12b$raw$est.gainloss) == estcat,] Let’s now look at gains. Here we see we are 95% confident that the gain of Other Vegetation from 2011 to 2014 was 2.6% +/- 2.0%. othveg.gainloss[, c("gain.CI95left", "gain.est", "gain.CI95right")] output ## gain.CI95left gain.est gain.CI95right ## OtherVegetation 0.5687271 2.606516 4.644305 Then the losses. Here we see we are 95% confident that the loss of Other Vegetation from 2011 to 2014 was 1.8% +/- 1.3%. othveg.gainloss[, c("loss.CI95left", "loss.est", "loss.CI95right")] output ## loss.CI95left loss.est loss.CI95right ## OtherVegetation 0.4829226 1.754386 3.025849 …and now the net change. Here we see we are 95% confident that the loss of Other Vegetation from 2011 to 2014 was 0.9% +/- 2.4%. othveg.gainloss[, c("diff.CI95left", "diff.est", "diff.CI95right")] output ## diff.CI95left diff.est diff.CI95right ## OtherVegetation -1.506734 0.8521303 3.210995 #### Example 8: Ratio to means estimates View Example In this example, we look at within category estimates, as the estimate proportion of one category within the estimated proportion of another category. Let’s first look at the proportion of land cover at Time 1 (2011) within land that changed in Davis and Salt Lake Counties, Utah. Here, we use the PBpoparea population dataset from Population Example 2 as the population dataset. First, we create a lookup table for the points defining changed land changelut <- data.frame(change_1_2=c(0,1,2), change_1_2nm=c("No Change", "Change", "Expected Change")) changelut output ## change_1_2 change_1_2nm ## 1 0 No Change ## 2 1 Change ## 3 2 Expected Change Now, using the PBpoparea population for both counties, let’s get our ratio estimate. chgcover1 <- modPB(PBpopdat = PBpoparea, ratio = TRUE, rowvar = "change_1_2", colvar = "cover_1", nonsamp.pntfilter = "cover_1 != 999", table_opts = list(rowlut=changelut, collut=icecover_1), title_opts = list(title.rowvar="Change")) Look at estimates chgcover1$est
output
##            Change Tree Shrub OtherVegetation Barren Impervious Water
## 1       No Change 21.1  13.4            21.1   11.1       12.3  20.9
## 2          Change  7.6   7.1            40.7   24.2        8.2  12.2
## 3 Expected Change  1.8    --            38.2     --         --    60

And percent sampling error

chgcover1$pse output ## Change Tree Shrub OtherVegetation Barren Impervious Water ## 1 No Change 11.71 17.39 11.32 21.12 18.16 16.18 ## 2 Change 77.09 87.74 29.49 37.32 52.03 53.95 ## 3 Expected Change 99.27 -- 44.21 -- -- 35.46 Now we can check sum of row estimates. Should sum to 100%. sum(as.numeric(chgcover1$est[1,-1]))
output
## [1] 99.9
sum(as.numeric(chgcover1$est[2,-1])) output ## [1] 100 Next, let’s generate estimates for percent land cover at Time 1 (2011) within agent of change in Davis and Salt Lake Counties, Utah. chg_ag_cover1.rat <- modPB(PBpopdat = PBpoparea, ratio = TRUE, rowvar = "chg_ag_2", colvar = "cover_1", nonsamp.pntfilter = "cover_1 != 999", table_opts = list(rowlut = icechg_ag, collut = icecover_1), title_opts = list(title.rowvar = "Change agent", title.colvar = "Land cover (2011)"), returntitle = TRUE) Look at estimates chg_ag_cover1.rat$est
output
##                    Change agent Tree Shrub OtherVegetation Barren Impervious
## 1                     No Change 19.5  11.8            24.8   11.3       11.5
## 2                   Development  2.6  19.5            34.2   35.1        8.7
## 3 Removal or Loss of Vegetation   --    --             100     --         --
## 4           Stress or Mortality  100    --              --     --         --
## 5               Expected Change   --    --            17.4    2.3         --
##   Water
## 1    21
## 2    --
## 3    --
## 4    --
## 5  80.3

And percent sampling error

chg_ag_cover1.rat$pse output ## Change agent Tree Shrub OtherVegetation Barren Impervious ## 1 No Change 10.86 17 8.39 18.47 16.86 ## 2 Development 99.26 99.26 59.2 50.14 70.9 ## 3 Removal or Loss of Vegetation -- -- 68.08 -- -- ## 4 Stress or Mortality 99.95 -- -- -- -- ## 5 Expected Change -- -- 46.68 99.7 -- ## Water ## 1 14.13 ## 2 -- ## 3 -- ## 4 -- ## 5 37.14 Add Total column to ratio estimates. Note: all rows should equal 100% chg_ag_cover1.rat$est$Total <- rowSums(apply(chg_ag_cover1.rat$est[,-1], 2, as.numeric),
na.rm = TRUE)
chg_ag_cover1.rat$est output ## Change agent Tree Shrub OtherVegetation Barren Impervious ## 1 No Change 19.5 11.8 24.8 11.3 11.5 ## 2 Development 2.6 19.5 34.2 35.1 8.7 ## 3 Removal or Loss of Vegetation -- -- 100 -- -- ## 4 Stress or Mortality 100 -- -- -- -- ## 5 Expected Change -- -- 17.4 2.3 -- ## Water Total ## 1 21 99.9 ## 2 -- 100.1 ## 3 -- 100.0 ## 4 -- 100.0 ## 5 80.3 100.0 Now compare nonraio and ratio to means estimates # Nonratio estimates chg_ag_cover1$est
output
##                    Change agent Tree Shrub OtherVegetation Barren Impervious
## 1                     No Change 17.8  10.8            22.4   12.7       10.4
## 2                   Development   --    --             1.3    1.5        1.1
## 3 Removal or Loss of Vegetation   --    --              --    0.1         --
## 4           Stress or Mortality  0.1    --              --     --         --
## 5               Expected Change   --     0               2    2.6         --
## 6                         Total 17.8  10.8            25.7   16.9       11.5
##   Water Total
## 1  16.9    91
## 2    --   3.9
## 3    --   0.1
## 4    --   0.1
## 5   0.4     5
## 6  17.2   100
# Ratio to means estimates
chg_ag_cover1.rat$est output ## Change agent Tree Shrub OtherVegetation Barren Impervious ## 1 No Change 19.5 11.8 24.8 11.3 11.5 ## 2 Development 2.6 19.5 34.2 35.1 8.7 ## 3 Removal or Loss of Vegetation -- -- 100 -- -- ## 4 Stress or Mortality 100 -- -- -- -- ## 5 Expected Change -- -- 17.4 2.3 -- ## Water Total ## 1 21 99.9 ## 2 -- 100.1 ## 3 -- 100.0 ## 4 -- 100.0 ## 5 80.3 100.0 Let’s look at the percent of land cover at Time 2 within the percent of land cover at Time 1 in Davis and Salt Lake Counties, to look more closely at percent transition changes within categories. cover1_2.rat <- modPB(PBpopdat = PBpoparea, ratio = TRUE, rowvar = "cover_1", colvar = "cover_2", nonsamp.pntfilter = "cover_1 != 999", table_opts = list(rowlut = icecover_1, collut = icecover_2), title_opts = list(title.rowvar = "Land cover (2011)", title.colvar = "Land cover (2014)"), returntitle=TRUE) Look at estimates. cover1_2.rat$est
output
##   Land cover (2011) Tree Shrub OtherVegetation Barren Impervious Water
## 1              Tree 99.4    --             0.3    0.1        0.2    --
## 2             Shrub   --  93.5             4.2    2.3         --    --
## 3   OtherVegetation   --   0.1              93      4        1.4   1.5
## 4            Barren   --    --             3.3   93.6        3.1    --
## 5        Impervious   --    --              --    0.2       99.8    --
## 6             Water   --    --             7.3   19.8         --  72.9

We can also display the estimates in a stacked bar plot, with the datBarStacked() function in FIESTA. We will use the unit_grpest table from the raw data.

datBarStacked(x = cover1_2.rat$raw$unit_grpest,
main.attribute = "Land cover (2011)",
sub.attribute = "Land cover (2014)",
response = "est",
xlabel = "Land Cover (2011)",
legend.title = "Land Cover (2014)")

Now, let’s only look at change by subsetting the columns of unit_grpest to table cells that indicate change. In this example, change is where Land cover in 2011 is not equal to Land cover in 2014.

x <- cover1_2.rat$raw$unit_grpest
x <- x[x$'Land cover (2011)' != x$'Land cover (2014)',]

datBarStacked(x = x,
main.attribute = "Land cover (2011)",
sub.attribute = "Land cover (2014)",
response = "est",
xlabel = "Land Cover (2011)",
legend.title = "Land Cover (2014)",
main.order = rev(c("Tree", "Shrub", "OtherVegetation",
"Impervious", "Barren", "Water")))

#### Example 9: Plot-level Data, with percent by domain as separate columns

View Example

This example demonstrates generating estimates from data that are already compiled from point data to percentages by plot. The population datasets used in this example can be found in Population Example 4.

We can get estimates of percent land cover at Time 1 (2011) for all land in Davis and Salt Lake Counties, Utah.

pltpct11 <- modPB(PBpopdat = PBpctpop11,
title_opts = list(title.rowvar="Land cover (2011)"),
returntitle = TRUE)
pltpct11$est output ## Land cover (2011) Estimate Percent Sampling Error ## 1 Barren11 11.8 19.52 ## 2 Impervious11 10.8 19.39 ## 3 OtherVegetation11 24.9 11.72 ## 4 Shrub11 11.5 19.59 ## 5 Tree11 17.9 14.43 ## 6 Water11 23.1 15.47 We can also create a barplot with estimates and error bar, using the Percent Sampling Error column. datBarplot(x = pltpct11$est,
xvar = "Land cover (2011)",
yvar = "Estimate",
errbars = TRUE,
psevar = "Percent Sampling Error")
plot

Note that we have many options to choose from when creating the barplot. This time use data from the raw data with the standard error (est.se) column and add labels and a title.

datBarplot(x = pltpct11$raw$unit_rowest,
xvar = "Land cover (2011)",
yvar = "est",
errbars = TRUE,
sevar = "est.se",
ylim = c(0,30),
ylabel = "Percent of land",
main = "Percent cover at Time 1 (2011)")
plot

Now, let’s get area estimates of land cover at Time 1 (2011) for all land in Davis and Salt Lake Counties, Utah by adding tabtype = "AREA" to the modPB() call.

pltpct11.area <- modPB(PBpopdat = PBpctpop11,
tabtype = "AREA",
returntitle = TRUE)
pltpct11.area$est output ## variable Estimate Percent Sampling Error ## 1 Barren11 108693.6 19.52 ## 2 Impervious11 99260.1 19.39 ## 3 OtherVegetation11 229803.4 11.72 ## 4 Shrub11 106404.6 19.59 ## 5 Tree11 165225.4 14.43 ## 6 Water11 213294.7 15.47 We can of course us the population dataset for Time 2 (2014) that we created in Population Example 4 to produce estimates for Time 2. Below we produce estimates of percent land cover at Time 2 (2014) for all land in Davis and Salt Lake Counties, Utah. pltpct14 <- modPB(PBpopdat = PBpctpop14, returntitle = TRUE) pltpct14$est
output
##            variable Estimate Percent Sampling Error
## 1          Barren14     16.9                  16.36
## 2      Impervious14     11.5                  18.44
## 3 OtherVegetation14     25.7                  11.59
## 4           Shrub14     10.8                  19.96
## 5            Tree14     17.8                  14.52
## 6           Water14     17.2                  18.71

Next we have estimates of area of land cover at Time 2 (2014) for all land in Davis and Salt Lake Counties, Utah by adding tabtype = "AREA".

pltpct14.area <- modPB(PBpopdat = PBpctpop14,
tabtype = "AREA",
returntitle = TRUE)
pltpct14.area$est output ## variable Estimate Percent Sampling Error ## 1 Barren14 155861.2 16.36 ## 2 Impervious14 106057.8 18.44 ## 3 OtherVegetation14 237502.8 11.59 ## 4 Shrub14 99745.6 19.96 ## 5 Tree14 164323.6 14.52 ## 6 Water14 159052 18.71 Again, we can look at other population data that we created in Population Example 4. Let’s also look at transitions. In this example we will generate estimates of percent land cover change from vegetated to non-vegetated for all land in Davis and Salt Lake Counties, Utah by using the PBpctpop.veg object as our population dataset. This transition was recorded in the initial dataset (i.e., Veg.NonVeg). Then, get estimates. We can add a title in the title_opts parameter to help describe the estimate. pltpct.veg <- modPB(PBpopdat = PBpctpop.veg, title_opts = list(title.rowvar = "Veg to NonVeg transition"), returntitle = TRUE) pltpct.veg$est
output
##   Veg to NonVeg transition Estimate Percent Sampling Error
## 1               Veg.NonVeg      1.8                  40.18

#### Example 10: Point-level transition data (T1 Cover - T2 Cover) - Post-Stratification

View Example

This example shows how we can add post-stratification to reduce the variance (i.e, increase precision) in the estimates. The population data for this example were created in Population Example 5.

Now, we can produce the estimates.

cover12ps <- modPB(PBpopdat = PBpopareaPS,
rowvar = "cover_1",
colvar = "cover_2",
nonsamp.pntfilter = "cover_1 != 999",
table_opts = list(rowlut = icecover_1,
collut = icecover_2),
title_opts = list(title.rowvar = "Land Cover"))

Let’s again get estimates without strata. Again, we use (different) population data that were created in Population Example 5.

cover12 <- modPB(PBpopdat = PBpoparea_nonPS,
rowvar = "cover_1",
colvar = "cover_2",
nonsamp.pntfilter = "cover_1 != 999",
table_opts = list(rowlut = icecover_1,
collut = icecover_2),
title_opts = list(title.rowvar = "Land Cover"))

Finally, let’s compare estimates.

cover12$est output ## Land Cover Tree Shrub OtherVegetation Barren Impervious Water Total ## 1 Tree 17.8 -- 0.1 0 0 -- 17.9 ## 2 Shrub -- 10.8 0.5 0.3 -- -- 11.5 ## 3 OtherVegetation -- 0 23.1 1 0.4 0.4 24.9 ## 4 Barren -- -- 0.4 11 0.4 -- 11.8 ## 5 Impervious -- -- -- 0 10.7 -- 10.8 ## 6 Water -- -- 1.7 4.6 -- 16.9 23.1 ## 7 Total 17.8 10.8 25.7 16.9 11.5 17.2 100 cover12ps$est
output
##        Land Cover Tree Shrub OtherVegetation Barren Impervious Water Total
## 1            Tree 18.5    --               0      0          0    --  18.6
## 2           Shrub   --  11.1             0.5    0.3         --    --  11.8
## 3 OtherVegetation   --     0            22.9      1        0.3   0.4  24.6
## 4          Barren   --    --             0.4   10.9        0.4    --  11.6
## 5      Impervious   --    --              --      0       10.6    --  10.6
## 6           Water   --    --             1.7    4.5         --  16.5  22.7
## 7           Total 18.5  11.1            25.4   16.6       11.4  16.9   100
cover12$pse output ## Land Cover Tree Shrub OtherVegetation Barren Impervious Water Total ## 1 Tree 14.53 -- 100 100 100 -- 14.43 ## 2 Shrub -- 20.03 100 100 -- -- 19.6 ## 3 OtherVegetation -- 100 12.02 44.98 71.17 62.79 11.72 ## 4 Barren -- -- 65.47 20.16 58.14 -- 19.53 ## 5 Impervious -- -- -- 100 19.41 -- 19.38 ## 6 Water -- -- 53.25 39.46 -- 19.16 15.47 ## 7 Total 14.53 19.96 11.58 16.36 18.44 18.71 0 cover12ps$pse
output
##        Land Cover  Tree  Shrub OtherVegetation Barren Impervious Water Total
## 1            Tree 12.44     --          101.01 101.01     101.01    -- 12.37
## 2           Shrub    --  19.41          101.01 101.01         --    -- 19.09
## 3 OtherVegetation    -- 101.01           11.99  45.32      71.85 63.37 11.66
## 4          Barren    --     --           66.08  20.14      58.66    --  19.5
## 5      Impervious    --     --              -- 101.01      19.55    -- 19.52
## 6           Water    --     --            53.7  39.73         -- 19.05 15.25
## 7           Total 12.44  19.35           11.51  16.25      18.56 18.58     0

## References

Frescino, Tracey S.; Moisen, Gretchen G.; Megown, Kevin A.; Nelson, Val J.; Freeman, Elizabeth A.; Patterson, Paul L.; Finco, Mark; Brewer, Ken; Menlove, James 2009. Nevada Photo-Based Inventory Pilot (NPIP) photo sampling procedures. Gen. Tech. Rep. RMRS-GTR-222. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 30 p.

Patterson, Paul L. 2012. Photo-based estimators for the Nevada photo-based inventory. Res. Pap. RMRS-RP-92. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 14 p.

Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul L.; Freeman, Elizabeth A.; Patterson, Paul L.; Menlove, James. In Press.. Nevada Photo-Based Inventory Pilot (NPIP) resource estimates. Gen. Tech. Rep. RMRS-GTR-344. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 63 p.